I suspect it's a lot of that too. Essentially pattern matching.

I work on web crawlers and data mining at scale and well over 50% of my code output is written by AI. I use mostly o1 (copying and pasting isolated snippets) or Jetbrains' AI service.

I also have access to a full-service "junior developer" AI that can take in an entire git repo at once, and its code outputs are significantly less useful -- maybe 10%.

I think a lot of peoples' success rate with AI boils down to their choices in language/toolkit (AI does much better the more common it is) and how they prompt it.

Note that you still need an experienced set of eyes supervising, the thought of an LLM committing to a git repo without a human in the loop scares me.

Have you tried the AI intellisense models like Copilot?

I don't understand the notion that it is faster to generate repetitive code with keyboard macros. I use Vim-mode exclusively, and while I'm not a Vim master, I don't think there's any set of macros that will do what Copilot can do.

It's not that Copilot is smart. It's that 60% of what I do doesn't require much intelligence to anticipate. It is the 40% that matters, the remainder can be trivially guessed, and this is exactly what Copilot does.

Maybe this will help: you need to imagine with an AI intellisense that with each keystroke, you are collapsing the possibility space down to a smaller, finite number of outcomes. You write exactly what code you need for the dumb AI to predict the rest of it.

There are a LOT of reasons why AI intellisense is not all there yet; it can be distracting; it can try to generate too much at once; none of the tools have LSP integrated, so it will provide bullshit suggestions of library methods that don't exist. This is all true, and yet it is still highly valuable in some domains, for some people.

That said, if you write x86 assembly for a living, you are probably out of luck.

(I write Kotlin, Java for Android apps and services, C++ that is tightly integrated with the SoC. Python and Bash for command-line tools that invoke REST APIs. Copilot is useful for these domains.)

You might not be getting gaslit.

I’ve sat through some interviews recently with candidates who started their careers in the last 6 years or so… during the boom cycle. Some were quite good but a troubling amount were clearly over-leveled at their current/previous employers.

For example, last month we interviewed someone for a Staff Engineering role (current role: L5 Senior II engineer), for Python. This person was unable to explain what a set was in Python, didn’t seem to grok the basic HTTP request/response pattern etc. This wasn’t a leetcode interview; it was an engineering conversation. It was the same questions we’d given dozens and dozens engineers in the past. It wasn’t a language barrier issue (guy was American, interviewer was American). Dude just seemed to have a very very narrow set of skills.

For people like this I imagine AI feels like a superpower.

> but that kind of code is so easy to write, and code is already way more terse than natural language! it's literally more typing to explain to an LLM how to write some greenfield web CRUD than it is to just type out the code, and if there's a lot of boilerplate it's faster to generate the repetitive parts with keyboard macros!

> where's the value everyone on this site and on LinkedIn (but NONE in my real or professional life) seems to get?

I can remember how to describe that every time I need to make a button. I can’t remember the new flavor of the months special snowflake way of expressing that. I’ve had decent traction just listing the pieces in my stack and then subbing those out whenever it changes

> it's literally more typing to explain to an LLM how to write some greenfield web CRUD than it is to just type out the code, and if there's a lot of boilerplate it's faster to generate the repetitive parts with keyboard macros!

I mostly agree with you, but I do think it's faster than searching for and finding the boilerplate you need. I also think AI code completions and the ability to use it to generate the small blocks you will put together into the main app are helpful. Idk, it's not a nothing burger. It's not going to start working at AWS either.

I work in machine learning research: training loops and loss functions are incredibly repetitive and pattern filled, highly represented in the code the LLMs are trained on, and typically short. They are exactly my intuition of simple code that LLMs would work well on.

With respect, having trialed these tools on pretty large ML codebases it's very much most folks' experiences that they're not very good across the board.

Training loops, sure... those are pretty much straight pattern recognition w/ well-represented APIs. But more broadly? Not so much.

I didn't say it cannot work well on anything other than greenfield web projects. I said it would probably be best at those as those have the most training data available. It can work well for your use case and still fit the pattern I laid out